Upload rule_engine.py

core/rule_engine.py

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+"""
+Rule Engine — Deterministic Design System Analysis
+===================================================
+
+This module handles ALL calculations that don't need LLM reasoning:
+- Type scale detection
+- AA/AAA contrast checking
+- Algorithmic color fixes
+- Spacing grid detection
+- Color statistics and deduplication
+
+LLMs should ONLY be used for:
+- Brand color identification (requires context understanding)
+- Palette cohesion (subjective assessment)
+- Design maturity scoring (holistic evaluation)
+- Prioritized recommendations (business reasoning)
+"""
+
+import colorsys
+import re
+from dataclasses import dataclass, field
+from functools import reduce
+from math import gcd
+from typing import Optional
+
+
+# =============================================================================
+# DATA CLASSES
+# =============================================================================
+
+@dataclass
+class TypeScaleAnalysis:
+    """Results of type scale analysis."""
+    detected_ratio: float
+    closest_standard_ratio: float
+    scale_name: str
+    is_consistent: bool
+    variance: float
+    sizes_px: list[float]
+    ratios_between_sizes: list[float]
+    recommendation: float
+    recommendation_name: str
+    
+    def to_dict(self) -> dict:
+        return {
+            "detected_ratio": round(self.detected_ratio, 3),
+            "closest_standard_ratio": self.closest_standard_ratio,
+            "scale_name": self.scale_name,
+            "is_consistent": self.is_consistent,
+            "variance": round(self.variance, 3),
+            "sizes_px": self.sizes_px,
+            "recommendation": self.recommendation,
+            "recommendation_name": self.recommendation_name,
+        }
+
+
+@dataclass
+class ColorAccessibility:
+    """Accessibility analysis for a single color."""
+    hex_color: str
+    name: str
+    contrast_on_white: float
+    contrast_on_black: float
+    passes_aa_normal: bool  # 4.5:1
+    passes_aa_large: bool   # 3.0:1
+    passes_aaa_normal: bool # 7.0:1
+    best_text_color: str    # White or black
+    suggested_fix: Optional[str] = None
+    suggested_fix_contrast: Optional[float] = None
+    
+    def to_dict(self) -> dict:
+        return {
+            "color": self.hex_color,
+            "name": self.name,
+            "contrast_white": round(self.contrast_on_white, 2),
+            "contrast_black": round(self.contrast_on_black, 2),
+            "aa_normal": self.passes_aa_normal,
+            "aa_large": self.passes_aa_large,
+            "aaa_normal": self.passes_aaa_normal,
+            "best_text": self.best_text_color,
+            "suggested_fix": self.suggested_fix,
+            "suggested_fix_contrast": round(self.suggested_fix_contrast, 2) if self.suggested_fix_contrast else None,
+        }
+
+
+@dataclass
+class SpacingGridAnalysis:
+    """Results of spacing grid analysis."""
+    detected_base: int
+    is_aligned: bool
+    alignment_percentage: float
+    misaligned_values: list[int]
+    recommendation: int
+    recommendation_reason: str
+    current_values: list[int]
+    suggested_scale: list[int]
+    
+    def to_dict(self) -> dict:
+        return {
+            "detected_base": self.detected_base,
+            "is_aligned": self.is_aligned,
+            "alignment_percentage": round(self.alignment_percentage, 1),
+            "misaligned_values": self.misaligned_values,
+            "recommendation": self.recommendation,
+            "recommendation_reason": self.recommendation_reason,
+            "current_values": self.current_values,
+            "suggested_scale": self.suggested_scale,
+        }
+
+
+@dataclass
+class ColorStatistics:
+    """Statistical analysis of color palette."""
+    total_count: int
+    unique_count: int
+    duplicate_count: int
+    gray_count: int
+    saturated_count: int
+    near_duplicates: list[tuple[str, str, float]]  # (color1, color2, similarity)
+    hue_distribution: dict[str, int]  # {"red": 5, "blue": 3, ...}
+    
+    def to_dict(self) -> dict:
+        return {
+            "total": self.total_count,
+            "unique": self.unique_count,
+            "duplicates": self.duplicate_count,
+            "grays": self.gray_count,
+            "saturated": self.saturated_count,
+            "near_duplicates_count": len(self.near_duplicates),
+            "hue_distribution": self.hue_distribution,
+        }
+
+
+@dataclass 
+class RuleEngineResults:
+    """Complete rule engine analysis results."""
+    typography: TypeScaleAnalysis
+    accessibility: list[ColorAccessibility]
+    spacing: SpacingGridAnalysis
+    color_stats: ColorStatistics
+    
+    # Summary
+    aa_failures: int
+    consistency_score: int  # 0-100
+    
+    def to_dict(self) -> dict:
+        return {
+            "typography": self.typography.to_dict(),
+            "accessibility": [a.to_dict() for a in self.accessibility if not a.passes_aa_normal],
+            "accessibility_all": [a.to_dict() for a in self.accessibility],
+            "spacing": self.spacing.to_dict(),
+            "color_stats": self.color_stats.to_dict(),
+            "summary": {
+                "aa_failures": self.aa_failures,
+                "consistency_score": self.consistency_score,
+            }
+        }
+
+
+# =============================================================================
+# COLOR UTILITIES
+# =============================================================================
+
+def hex_to_rgb(hex_color: str) -> tuple[int, int, int]:
+    """Convert hex to RGB tuple."""
+    hex_color = hex_color.lstrip('#')
+    if len(hex_color) == 3:
+        hex_color = ''.join([c*2 for c in hex_color])
+    return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
+
+
+def rgb_to_hex(r: int, g: int, b: int) -> str:
+    """Convert RGB to hex string."""
+    r = max(0, min(255, r))
+    g = max(0, min(255, g))
+    b = max(0, min(255, b))
+    return f"#{r:02x}{g:02x}{b:02x}"
+
+
+def get_relative_luminance(hex_color: str) -> float:
+    """Calculate relative luminance per WCAG 2.1."""
+    r, g, b = hex_to_rgb(hex_color)
+    
+    def channel_luminance(c):
+        c = c / 255
+        return c / 12.92 if c <= 0.03928 else ((c + 0.055) / 1.055) ** 2.4
+    
+    return 0.2126 * channel_luminance(r) + 0.7152 * channel_luminance(g) + 0.0722 * channel_luminance(b)
+
+
+def get_contrast_ratio(color1: str, color2: str) -> float:
+    """Calculate WCAG contrast ratio between two colors."""
+    l1 = get_relative_luminance(color1)
+    l2 = get_relative_luminance(color2)
+    lighter = max(l1, l2)
+    darker = min(l1, l2)
+    return (lighter + 0.05) / (darker + 0.05)
+
+
+def is_gray(hex_color: str, threshold: float = 0.1) -> bool:
+    """Check if color is a gray (low saturation)."""
+    r, g, b = hex_to_rgb(hex_color)
+    h, s, v = colorsys.rgb_to_hsv(r/255, g/255, b/255)
+    return s < threshold
+
+
+def get_saturation(hex_color: str) -> float:
+    """Get saturation value (0-1)."""
+    r, g, b = hex_to_rgb(hex_color)
+    h, s, v = colorsys.rgb_to_hsv(r/255, g/255, b/255)
+    return s
+
+
+def get_hue_name(hex_color: str) -> str:
+    """Get human-readable hue name."""
+    r, g, b = hex_to_rgb(hex_color)
+    h, s, v = colorsys.rgb_to_hsv(r/255, g/255, b/255)
+    
+    if s < 0.1:
+        return "gray"
+    
+    hue_deg = h * 360
+    
+    if hue_deg < 15 or hue_deg >= 345:
+        return "red"
+    elif hue_deg < 45:
+        return "orange"
+    elif hue_deg < 75:
+        return "yellow"
+    elif hue_deg < 150:
+        return "green"
+    elif hue_deg < 210:
+        return "cyan"
+    elif hue_deg < 270:
+        return "blue"
+    elif hue_deg < 315:
+        return "purple"
+    else:
+        return "pink"
+
+
+def color_distance(hex1: str, hex2: str) -> float:
+    """Calculate perceptual color distance (0-1, lower = more similar)."""
+    r1, g1, b1 = hex_to_rgb(hex1)
+    r2, g2, b2 = hex_to_rgb(hex2)
+    
+    # Simple Euclidean distance in RGB space (normalized)
+    dr = (r1 - r2) / 255
+    dg = (g1 - g2) / 255
+    db = (b1 - b2) / 255
+    
+    return (dr**2 + dg**2 + db**2) ** 0.5 / (3 ** 0.5)
+
+
+def darken_color(hex_color: str, factor: float) -> str:
+    """Darken a color by a factor (0-1)."""
+    r, g, b = hex_to_rgb(hex_color)
+    r = int(r * (1 - factor))
+    g = int(g * (1 - factor))
+    b = int(b * (1 - factor))
+    return rgb_to_hex(r, g, b)
+
+
+def lighten_color(hex_color: str, factor: float) -> str:
+    """Lighten a color by a factor (0-1)."""
+    r, g, b = hex_to_rgb(hex_color)
+    r = int(r + (255 - r) * factor)
+    g = int(g + (255 - g) * factor)
+    b = int(b + (255 - b) * factor)
+    return rgb_to_hex(r, g, b)
+
+
+def find_aa_compliant_color(hex_color: str, background: str = "#ffffff", target_contrast: float = 4.5) -> str:
+    """
+    Algorithmically adjust a color until it meets AA contrast requirements.
+    
+    Returns the original color if it already passes, otherwise returns
+    a darkened/lightened version that passes.
+    """
+    current_contrast = get_contrast_ratio(hex_color, background)
+    
+    if current_contrast >= target_contrast:
+        return hex_color
+    
+    # Determine if we need to darken or lighten
+    bg_luminance = get_relative_luminance(background)
+    color_luminance = get_relative_luminance(hex_color)
+    
+    # If background is light, darken the color; if dark, lighten it
+    should_darken = bg_luminance > 0.5
+    
+    best_color = hex_color
+    best_contrast = current_contrast
+    
+    for i in range(1, 101):
+        factor = i / 100
+        
+        if should_darken:
+            new_color = darken_color(hex_color, factor)
+        else:
+            new_color = lighten_color(hex_color, factor)
+        
+        new_contrast = get_contrast_ratio(new_color, background)
+        
+        if new_contrast >= target_contrast:
+            return new_color
+        
+        if new_contrast > best_contrast:
+            best_contrast = new_contrast
+            best_color = new_color
+    
+    return best_color
+
+
+# =============================================================================
+# TYPE SCALE ANALYSIS
+# =============================================================================
+
+# Standard type scale ratios
+STANDARD_SCALES = {
+    1.067: "Minor Second",
+    1.125: "Major Second",
+    1.200: "Minor Third",
+    1.250: "Major Third",       # ⭐ Recommended
+    1.333: "Perfect Fourth",
+    1.414: "Augmented Fourth",
+    1.500: "Perfect Fifth",
+    1.618: "Golden Ratio",
+    2.000: "Octave",
+}
+
+
+def parse_size_to_px(size: str) -> Optional[float]:
+    """Convert any size string to pixels."""
+    if isinstance(size, (int, float)):
+        return float(size)
+    
+    size = str(size).strip().lower()
+    
+    # Extract number
+    match = re.search(r'([\d.]+)', size)
+    if not match:
+        return None
+    
+    value = float(match.group(1))
+    
+    if 'rem' in size:
+        return value * 16  # Assume 16px base
+    elif 'em' in size:
+        return value * 16  # Approximate
+    elif 'px' in size or size.replace('.', '').isdigit():
+        return value
+    
+    return value
+
+
+def analyze_type_scale(typography_tokens: dict) -> TypeScaleAnalysis:
+    """
+    Analyze typography tokens to detect type scale ratio.
+    
+    Args:
+        typography_tokens: Dict of typography tokens with font_size
+        
+    Returns:
+        TypeScaleAnalysis with detected ratio and recommendations
+    """
+    # Extract and parse sizes
+    sizes = []
+    for name, token in typography_tokens.items():
+        if isinstance(token, dict):
+            size = token.get("font_size") or token.get("fontSize") or token.get("size")
+        else:
+            size = getattr(token, "font_size", None)
+        
+        if size:
+            px = parse_size_to_px(size)
+            if px and px > 0:
+                sizes.append(px)
+    
+    # Sort and dedupe
+    sizes_px = sorted(set(sizes))
+    
+    if len(sizes_px) < 2:
+        return TypeScaleAnalysis(
+            detected_ratio=1.0,
+            closest_standard_ratio=1.25,
+            scale_name="Unknown",
+            is_consistent=False,
+            variance=0,
+            sizes_px=sizes_px,
+            ratios_between_sizes=[],
+            recommendation=1.25,
+            recommendation_name="Major Third",
+        )
+    
+    # Calculate ratios between consecutive sizes
+    ratios = []
+    for i in range(len(sizes_px) - 1):
+        if sizes_px[i] > 0:
+            ratio = sizes_px[i + 1] / sizes_px[i]
+            if 1.0 < ratio < 3.0:  # Reasonable range
+                ratios.append(ratio)
+    
+    if not ratios:
+        return TypeScaleAnalysis(
+            detected_ratio=1.0,
+            closest_standard_ratio=1.25,
+            scale_name="Unknown",
+            is_consistent=False,
+            variance=0,
+            sizes_px=sizes_px,
+            ratios_between_sizes=[],
+            recommendation=1.25,
+            recommendation_name="Major Third",
+        )
+    
+    # Average ratio
+    avg_ratio = sum(ratios) / len(ratios)
+    
+    # Variance (consistency check)
+    variance = max(ratios) - min(ratios) if ratios else 0
+    is_consistent = variance < 0.15  # Within 15% variance is "consistent"
+    
+    # Find closest standard scale
+    closest_scale = min(STANDARD_SCALES.keys(), key=lambda x: abs(x - avg_ratio))
+    scale_name = STANDARD_SCALES[closest_scale]
+    
+    # Recommendation
+    if is_consistent and abs(avg_ratio - closest_scale) < 0.05:
+        # Already using a standard scale
+        recommendation = closest_scale
+        recommendation_name = scale_name
+    else:
+        # Recommend Major Third (1.25) as default
+        recommendation = 1.25
+        recommendation_name = "Major Third"
+    
+    return TypeScaleAnalysis(
+        detected_ratio=avg_ratio,
+        closest_standard_ratio=closest_scale,
+        scale_name=scale_name,
+        is_consistent=is_consistent,
+        variance=variance,
+        sizes_px=sizes_px,
+        ratios_between_sizes=ratios,
+        recommendation=recommendation,
+        recommendation_name=recommendation_name,
+    )
+
+
+# =============================================================================
+# ACCESSIBILITY ANALYSIS
+# =============================================================================
+
+def analyze_accessibility(color_tokens: dict) -> list[ColorAccessibility]:
+    """
+    Analyze all colors for WCAG accessibility compliance.
+    
+    Args:
+        color_tokens: Dict of color tokens with value/hex
+        
+    Returns:
+        List of ColorAccessibility results
+    """
+    results = []
+    
+    for name, token in color_tokens.items():
+        if isinstance(token, dict):
+            hex_color = token.get("value") or token.get("hex") or token.get("color")
+        else:
+            hex_color = getattr(token, "value", None)
+        
+        if not hex_color or not hex_color.startswith("#"):
+            continue
+        
+        try:
+            contrast_white = get_contrast_ratio(hex_color, "#ffffff")
+            contrast_black = get_contrast_ratio(hex_color, "#000000")
+            
+            passes_aa_normal = contrast_white >= 4.5 or contrast_black >= 4.5
+            passes_aa_large = contrast_white >= 3.0 or contrast_black >= 3.0
+            passes_aaa_normal = contrast_white >= 7.0 or contrast_black >= 7.0
+            
+            best_text = "#ffffff" if contrast_white > contrast_black else "#000000"
+            
+            # Generate fix suggestion if needed
+            suggested_fix = None
+            suggested_fix_contrast = None
+            
+            if not passes_aa_normal:
+                suggested_fix = find_aa_compliant_color(hex_color, "#ffffff", 4.5)
+                suggested_fix_contrast = get_contrast_ratio(suggested_fix, "#ffffff")
+            
+            results.append(ColorAccessibility(
+                hex_color=hex_color,
+                name=name,
+                contrast_on_white=contrast_white,
+                contrast_on_black=contrast_black,
+                passes_aa_normal=passes_aa_normal,
+                passes_aa_large=passes_aa_large,
+                passes_aaa_normal=passes_aaa_normal,
+                best_text_color=best_text,
+                suggested_fix=suggested_fix,
+                suggested_fix_contrast=suggested_fix_contrast,
+            ))
+        except Exception:
+            continue
+    
+    return results
+
+
+# =============================================================================
+# SPACING GRID ANALYSIS
+# =============================================================================
+
+def analyze_spacing_grid(spacing_tokens: dict) -> SpacingGridAnalysis:
+    """
+    Analyze spacing tokens to detect grid alignment.
+    
+    Args:
+        spacing_tokens: Dict of spacing tokens with value_px or value
+        
+    Returns:
+        SpacingGridAnalysis with detected grid and recommendations
+    """
+    values = []
+    
+    for name, token in spacing_tokens.items():
+        if isinstance(token, dict):
+            px = token.get("value_px") or token.get("value")
+        else:
+            px = getattr(token, "value_px", None) or getattr(token, "value", None)
+        
+        if px:
+            try:
+                px_val = int(float(str(px).replace('px', '')))
+                if px_val > 0:
+                    values.append(px_val)
+            except:
+                continue
+    
+    if not values:
+        return SpacingGridAnalysis(
+            detected_base=8,
+            is_aligned=False,
+            alignment_percentage=0,
+            misaligned_values=[],
+            recommendation=8,
+            recommendation_reason="No spacing values detected, defaulting to 8px grid",
+            current_values=[],
+            suggested_scale=[0, 4, 8, 12, 16, 20, 24, 32, 40, 48, 64],
+        )
+    
+    values = sorted(set(values))
+    
+    # Find GCD (greatest common divisor) of all values
+    detected_base = reduce(gcd, values)
+    
+    # Check alignment to common grids (4px, 8px)
+    aligned_to_4 = all(v % 4 == 0 for v in values)
+    aligned_to_8 = all(v % 8 == 0 for v in values)
+    
+    # Find misaligned values (not divisible by detected base)
+    misaligned = [v for v in values if v % detected_base != 0] if detected_base > 1 else values
+    
+    alignment_percentage = (len(values) - len(misaligned)) / len(values) * 100 if values else 0
+    
+    # Determine recommendation
+    if aligned_to_8:
+        recommendation = 8
+        recommendation_reason = "All values already align to 8px grid"
+        is_aligned = True
+    elif aligned_to_4:
+        recommendation = 4
+        recommendation_reason = "Values align to 4px grid (consider 8px for simpler system)"
+        is_aligned = True
+    elif detected_base in [4, 8]:
+        recommendation = detected_base
+        recommendation_reason = f"Detected {detected_base}px base with {alignment_percentage:.0f}% alignment"
+        is_aligned = alignment_percentage >= 80
+    else:
+        recommendation = 8
+        recommendation_reason = f"Inconsistent spacing detected (GCD={detected_base}), recommend 8px grid"
+        is_aligned = False
+    
+    # Generate suggested scale
+    base = recommendation
+    suggested_scale = [0] + [base * i for i in [0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, 10, 12, 16] if base * i == int(base * i)]
+    suggested_scale = sorted(set([int(v) for v in suggested_scale]))
+    
+    return SpacingGridAnalysis(
+        detected_base=detected_base,
+        is_aligned=is_aligned,
+        alignment_percentage=alignment_percentage,
+        misaligned_values=misaligned,
+        recommendation=recommendation,
+        recommendation_reason=recommendation_reason,
+        current_values=values,
+        suggested_scale=suggested_scale,
+    )
+
+
+# =============================================================================
+# COLOR STATISTICS
+# =============================================================================
+
+def analyze_color_statistics(color_tokens: dict, similarity_threshold: float = 0.05) -> ColorStatistics:
+    """
+    Analyze color palette statistics.
+    
+    Args:
+        color_tokens: Dict of color tokens
+        similarity_threshold: Distance threshold for "near duplicate" (0-1)
+        
+    Returns:
+        ColorStatistics with palette analysis
+    """
+    colors = []
+    
+    for name, token in color_tokens.items():
+        if isinstance(token, dict):
+            hex_color = token.get("value") or token.get("hex")
+        else:
+            hex_color = getattr(token, "value", None)
+        
+        if hex_color and hex_color.startswith("#"):
+            colors.append(hex_color.lower())
+    
+    unique_colors = list(set(colors))
+    
+    # Count grays and saturated
+    grays = [c for c in unique_colors if is_gray(c)]
+    saturated = [c for c in unique_colors if get_saturation(c) > 0.3]
+    
+    # Find near duplicates
+    near_duplicates = []
+    for i, c1 in enumerate(unique_colors):
+        for c2 in unique_colors[i+1:]:
+            dist = color_distance(c1, c2)
+            if dist < similarity_threshold and dist > 0:
+                near_duplicates.append((c1, c2, round(dist, 4)))
+    
+    # Hue distribution
+    hue_dist = {}
+    for c in unique_colors:
+        hue = get_hue_name(c)
+        hue_dist[hue] = hue_dist.get(hue, 0) + 1
+    
+    return ColorStatistics(
+        total_count=len(colors),
+        unique_count=len(unique_colors),
+        duplicate_count=len(colors) - len(unique_colors),
+        gray_count=len(grays),
+        saturated_count=len(saturated),
+        near_duplicates=near_duplicates,
+        hue_distribution=hue_dist,
+    )
+
+
+# =============================================================================
+# MAIN ANALYSIS FUNCTION
+# =============================================================================
+
+def run_rule_engine(
+    typography_tokens: dict,
+    color_tokens: dict,
+    spacing_tokens: dict,
+    radius_tokens: dict = None,
+    shadow_tokens: dict = None,
+    log_callback: Optional[callable] = None,
+) -> RuleEngineResults:
+    """
+    Run complete rule-based analysis on design tokens.
+    
+    This is FREE (no LLM costs) and handles all deterministic calculations.
+    
+    Args:
+        typography_tokens: Dict of typography tokens
+        color_tokens: Dict of color tokens  
+        spacing_tokens: Dict of spacing tokens
+        radius_tokens: Dict of border radius tokens (optional)
+        shadow_tokens: Dict of shadow tokens (optional)
+        log_callback: Function to log messages
+        
+    Returns:
+        RuleEngineResults with all analysis data
+    """
+    
+    def log(msg: str):
+        if log_callback:
+            log_callback(msg)
+    
+    log("")
+    log("═" * 60)
+    log("⚙️  LAYER 1: RULE ENGINE (FREE - $0.00)")
+    log("═" * 60)
+    log("")
+    
+    # ─────────────────────────────────────────────────────────────
+    # Typography Analysis
+    # ─────────────────────────────────────────────────────────────
+    log("   📐 TYPE SCALE ANALYSIS")
+    log("   " + "─" * 40)
+    typography = analyze_type_scale(typography_tokens)
+    
+    consistency_icon = "✅" if typography.is_consistent else "⚠️"
+    log(f"   ├─ Detected Ratio: {typography.detected_ratio:.3f}")
+    log(f"   ├─ Closest Standard: {typography.scale_name} ({typography.closest_standard_ratio})")
+    log(f"   ├─ Consistent: {consistency_icon} {'Yes' if typography.is_consistent else f'No (variance: {typography.variance:.2f})'}")
+    log(f"   ├─ Sizes Found: {typography.sizes_px}")
+    log(f"   └─ 💡 Recommendation: {typography.recommendation} ({typography.recommendation_name})")
+    log("")
+    
+    # ─────────────────────────────────────────────────────────────
+    # Accessibility Analysis  
+    # ─────────────────────────────────────────────────────────────
+    log("   ♿ ACCESSIBILITY CHECK (WCAG AA/AAA)")
+    log("   " + "─" * 40)
+    accessibility = analyze_accessibility(color_tokens)
+    
+    failures = [a for a in accessibility if not a.passes_aa_normal]
+    passes = len(accessibility) - len(failures)
+    
+    log(f"   ├─ Colors Analyzed: {len(accessibility)}")
+    log(f"   ├─ AA Pass: {passes} ✅")
+    log(f"   ├─ AA Fail: {len(failures)} {'❌' if failures else '✅'}")
+    
+    if failures:
+        log("   │")
+        log("   │  ⚠️  FAILING COLORS:")
+        for i, f in enumerate(failures[:5]):
+            fix_info = f" → 💡 Fix: {f.suggested_fix} ({f.suggested_fix_contrast:.1f}:1)" if f.suggested_fix else ""
+            log(f"   │  ├─ {f.name}: {f.hex_color} ({f.contrast_on_white:.1f}:1 on white){fix_info}")
+        if len(failures) > 5:
+            log(f"   │  └─ ... and {len(failures) - 5} more")
+    
+    log("")
+    
+    # ─────────────────────────────────────────────────────────────
+    # Spacing Grid Analysis
+    # ─────────────────────────────────────────────────────────────
+    log("   📏 SPACING GRID ANALYSIS")
+    log("   " + "─" * 40)
+    spacing = analyze_spacing_grid(spacing_tokens)
+    
+    alignment_icon = "✅" if spacing.is_aligned else "⚠️"
+    log(f"   ├─ Detected Base: {spacing.detected_base}px")
+    log(f"   ├─ Grid Aligned: {alignment_icon} {spacing.alignment_percentage:.0f}%")
+    
+    if spacing.misaligned_values:
+        log(f"   ├─ Misaligned Values: {spacing.misaligned_values[:8]}{'...' if len(spacing.misaligned_values) > 8 else ''}")
+    
+    log(f"   ├─ Suggested Scale: {spacing.suggested_scale[:10]}...")
+    log(f"   └─ 💡 Recommendation: {spacing.recommendation}px ({spacing.recommendation_reason})")
+    log("")
+    
+    # ─────────────────────────────────────────────────────────────
+    # Color Statistics
+    # ─────────────────────────────────────────────────────────────
+    log("   🎨 COLOR PALETTE STATISTICS")
+    log("   " + "─" * 40)
+    color_stats = analyze_color_statistics(color_tokens)
+    
+    dup_icon = "⚠️" if color_stats.duplicate_count > 10 else "✅"
+    unique_icon = "⚠️" if color_stats.unique_count > 30 else "✅"
+    
+    log(f"   ├─ Total Colors: {color_stats.total_count}")
+    log(f"   ├─ Unique Colors: {color_stats.unique_count} {unique_icon}")
+    log(f"   ├─ Exact Duplicates: {color_stats.duplicate_count} {dup_icon}")
+    log(f"   ├─ Near-Duplicates: {len(color_stats.near_duplicates)}")
+    log(f"   ├─ Grays: {color_stats.gray_count} | Saturated: {color_stats.saturated_count}")
+    log(f"   └─ Hue Distribution: {dict(list(color_stats.hue_distribution.items())[:5])}...")
+    log("")
+    
+    # ─────────────────────────────────────────────────────────────
+    # Calculate Summary Scores
+    # ─────────────────────────────────────────────────────────────
+    
+    # Consistency score (0-100)
+    type_score = 25 if typography.is_consistent else 10
+    aa_score = 25 * (passes / max(len(accessibility), 1))
+    spacing_score = 25 * (spacing.alignment_percentage / 100)
+    color_score = 25 * (1 - min(color_stats.duplicate_count / max(color_stats.total_count, 1), 1))
+    
+    consistency_score = int(type_score + aa_score + spacing_score + color_score)
+    
+    log("   " + "─" * 40)
+    log(f"   📊 RULE ENGINE SUMMARY")
+    log(f"   ├─ Consistency Score: {consistency_score}/100")
+    log(f"   ├─ AA Failures: {len(failures)}")
+    log(f"   └─ Cost: $0.00 (free)")
+    log("")
+    
+    return RuleEngineResults(
+        typography=typography,
+        accessibility=accessibility,
+        spacing=spacing,
+        color_stats=color_stats,
+        aa_failures=len(failures),
+        consistency_score=consistency_score,
+    )